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发表于 2020-6-19 21:42:31
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- import numpy as np
- import pandas as pd
- import matplotlib.pyplot as plt
- #导入数据
- wine=pd.read_csv(open('wine-white_train.csv'))
- x=wine.iloc[:,0:11].values
- y=wine.iloc[:,11].values
- #留出法
- from sklearn.model_selection import train_test_split
- x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25)
- #数据预处理——标准化
- from sklearn.preprocessing import StandardScaler
- swine=StandardScaler()
- x_train=swine.fit_transform(x_train)
- x_test=swine.transform(x_test)
- #adaboost
- from sklearn.tree import DecisionTreeClassifier
- from sklearn.ensemble import AdaBoostClassifier
- bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1),
- algorithm="SAMME",
- n_estimators=2)
- bdt.fit(x_train,y_train)
- print(bdt.score(x_test,y_test))
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